Implementation of the MEDFRAT to decrease falls in community hospital ERs

19/12/2017

Source: Journal of Emergency Nursing, 2017, online

Follow this link for the abstract

Date of publication: November 2017

Publication type: Journal article

In a nutshell: This article looked to identify and implement and evidence-based fall-risk assessment tool for use in emergency departments in rural areas. The Memorial Emergency Department Fall-Risk Assessment Tool (MEDFRAT) was programmed into the electronic medical record along with interventions that could be selected for two fall-risk levels. The model was found to be a useful framework in the process of implementing evidence-based changes in a rural population, though ongoing follow-ups will determine if the process results in fewer falls.

Length of publication: 1 page

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.

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Self-powered fall detection system using pressure sensing triboelectric nanogenerators

17/10/2017

Source: Nano Energy, 2017, Vol. 41 p. 139-147

Follow this link for the abstract

Date of publication: November 2017

Publication type: Journal article

In a nutshell: Fall detection is becoming more important as the number of older people in society increases. People may fall at home where there is little timely help available, and falls themselves can cause injuries. Most fall detection technologies are inconvenient to wear, and visual or movement-based ones can be expensive and difficult to install. This study proposes a falls-detection system based on a pressure-sensing triboelectric nanogenerator array, which is cost-effective and ambient-based. It achieves a classification accuracy of 95.75% in identifying actual falls, and can be immediately installed due to low costs.

Length of publication: 8 pages

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.


Balance and fall risk assessments with mobile phone technology

15/09/2017

Source: Archives of Gerontology and Geriatrics, 2017, online

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Date of publication: August 2017

Publication type: Journal article

In a nutshell: While falls are a major health concern for older adults, preventative measures can help to reduce their incidence and severity; methods for assessing balance and fall risk factors are necessary to implement preventative measures. Mobile applications are being developed to enable seniors, caregivers and clinicians to monitor risks. This systematic review assesses reviews of such apps for their accuracy, reliability and validity. Further research is needed.

Length of publication: 16 pages

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.


Effects of an ICT-based fall-prevention system in community-dwelling older adults

16/06/2017

Source: International Journal of Human-Computer Studies, 2017, Vol 106 p. 10-25

Follow this link for the abstract

Date of publication: October 2017

Publication type: Journal article

In a nutshell: A sedentary lifestyle and low levels of physical activity are major factors in fall risk for older adults. ICT-based interventions could possibly counteract the risk for this group, as studies show that such interventions significantly reduce it. However, this population is heterogeneous, and several factors (such as gender, age, fitness and others) may influence the use of these systems. This study analyses the iStoppFalls system, testing effectiveness and usage indicators, among other things.

Length of publication: 15 pages

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.


A hierarchical alarm model for elderly fall prevention sensors

17/05/2017

Source: Pervasive and Mobile Computing, 2017, online

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Date of publication: April 2017

Publication type: Journal article

In a nutshell: New technologies allow for automatic monitoring of hospitalised older people, helping clinical staff to supervise to reduce falls. This paper introduces a hierarchical model to predict alarming states in a sensor worn over clothes. The hierarchy predicts levels of danger to warn clinical staff of possible fall danger.

Length of publication: 1 page

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to  find your local NHS Library.


Experiences with fall prevention technology within nursing homes

27/01/2017

Source: Geriatric Nursing, 2016, Online

Follow this link for the abstract

Date of publication: December 2016

Publication type: Journal article

In a nutshell: This joint US and Dutch study investigated how existing fall prevention technology was experienced within nursing home nurses’ environment and workflow. Two case reports were constructed from interview and observational data comparing the magnitude of falls, safety cultures and technology characteristics/effectiveness. Across cases, 1) a coordinated communication system was essential in facilitating effective fall prevention alert response, and 2) nursing home safety culture is tightly associated with the chosen technological system.

Length of publication: 1 page

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to fid your local NHS Library.


The use of ICT for falls prevention, detection and monitoring- call for case studies

16/09/2015

Source: Prevention of Falls Network Earth

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Date of publication: July 2015

Publication type: Website

In a nutshell: ProFouND, the EC-funded initiative dedicated to bringing about the dissemination and implementation of best practice in falls prevention across Europe, is looking for case studies from across the UK on peoples’ experiences of using information and ICT-based technologies for falls prediction, detection and prevention in practice. They want to know about technologies used and who they are manufactured by. These can include call alarm systems, bed alarms and even Wii or Kinect games systems – how have patients/older people got on with using the technology?

Length of publication: 1 page


Unobtrusive monitoring and identification of fall accidents

17/04/2015

Source: Medical Engineering and Physics, 2015, online

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Date of publication: March 2015

Publication type: Journal article

In a nutshell: The practical use of fall monitoring equipment is limited due to accuracy, usability, cost and the possible stigma attached to them. This paper proposes a fall sensor concept that can be embedded in the user’s footwear. It discusses algorithms, software and hardware developed. Software performance is illustrated using results of a series of functional tests, which show that the developed sensor can be used for the accurate measurement of various mobility and gait parameters and that falls are detected accurately.

Length of publication: 1 page

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.


Video footage and fall-incidence reports in long-term care.

19/03/2015

Source: Journal of the American Medical Directors Association, 2015, Online

Follow this link for the abstract

Date of publication: February 2015

Publication type: Journal article

In a nutshell: This study looked into how much agreement there was between reports of falls in long-term care facilities and video footage of the falls over a six year period. There was agreement between video footage and reports under half of the time. These technologies incorporating video capture or wearable sensors can improve our ability to understand the mechanisms involved in falls and to improve the prevention of falls in long-term care facilities.

Length of publication: 1 page

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.


Posture estimation and human support using wearable sensors and walking-aid robot

15/01/2015

Source: Robotics and Autonomous Systems, 2014, online

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Date of publication: December 2014

Publication type: Journal article

In a nutshell: This paper introduces a walking-aid robot that can automatically detect when a human, wearing sensors, is about to fall and act to stop it from happening. The robot takes reading of centre of pressure and centre of gravity from the person to predict possible falls and act accordingly.

Length of publication: 1 page

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.


An interactive voice response system for identifying older adults’ falls

15/01/2015

Source: Preventive medicine, 2015, Vol 71, p. 31-36

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Date of publication: February 2015

Publication type: Journal article

In a nutshell: Interactive voice response systems can collect data from a wide geographical area very easily and frequently. This study assessed the quality of the data collected from older people participating in a falls prevention programme evaluation in Pennsylvania. IVR was found to be a productive method of collecting data for falls research. Future research needs to establish the level of personal contact required to increase IVR completion rates.

Length of publication: 5 pages

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.


Recognition of gait cycle phases using wearable sensors

17/12/2014

Source: Robotics and Autonomous Systems, 2014, online

Follow this link for the abstract

Date of publication: October 2014

Publication type: Journal article

In a nutshell: This paper looks at the normal cycle of walking and gait using pressure and force measurements to ultimately help predict abnormal walking patterns which could lead to falling.

Length of publication: 1 page

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.